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HDP Algorithms for Trajectory Tracking and Formation Control of Multi-Agent Systems

IEEE ACCESS 2022年 10卷 27136-27146页

作者： Ferreira, Ernesto F. M. Da Fonseca Neto, Joao Viana Selmic, Rastko R. Fed Inst Maranhao Educ Dept BR-65930000 Acailandia Maranhao Brazil Univ Fed Maranhao Elect Engn Dept BR-65080805 Sao Luis Maranhao Brazil Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

A heuristic dynamic programming (HDP) algorithm for trajectory tracking and formation control of multi-agent systems (MAS) is presented in this paper. The selected HDP method allows for an online optimal control design. The multi-agent control problem is formulated as a leader-follower, where it is necessary for followers to maintain the assigned formation, while the leader follows the specified trajectory. Developed QR-Solver and RLS mu-QR-HDP-DLQR algorithms provide a new methodology to obtain the solution of the Hamilton-Jacobi-Bellman (HJB) equation. These proposed solutions are based on QR factorization to decrease the computational cost and avoid issues associated with numerical stability of conventional least squares (LS) method. The algorithms' performances such as convergence, numerical stability, and computational metrics, were experimentally evaluated for two control systems: aerial altitude control (one degree of freedom) and terrestrial robot (two degrees of freedom).

关键词： heuristic dynamic programming multi-agent systems leader-followers robots

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A wide area measurement based neurocontrol for generation excitation systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2009年第3期22卷 473-481页

作者： Ray, Swakshar Venayagamoorthy, Ganesh K. Missouri Univ Sci & Technol Dept Elect & Comp Engn Real Time Power & Intelligent Syst Lab Rolla MO 65409 USA Quanta Technol Raleigh NC 27607 USA

Power system is a highly interconnected nonlinear system that needs optimal and accurate control for continuous operation. Large power transfer through long transmission line between different electrical areas, stressed system and adverse interaction between local controllers, may give rise to slow frequency inter-area oscillations. The inter-area modes may not be visible from local measurements and hence it is useful to use remote measurement based centralized supplementary control. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control to local controllers such as automatic voltage regulators, power system stabilizers, etc. to damp out inter-area oscillations. This paper presents a design and real time implementation of a nonlinear neural network based optimal wide area controller using adaptive critic design (ACD). The real time implementation of a power system model is carried out on a real time digital simulator (RTDS). The performance of the WACS as a power system stability agent is studied using a two-area power system under different operating conditions and contingencies. The WACS shows improvement in the damping of inter-area mode with the use of supplementary excitation control. in addition, results show that the designed controller can provide robust performance under small communication delay in remote signal transmission. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Real time implementation heuristic dynamic programming Neural networks Optimal control Power system stability Wide area control

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A computational approach to optimal damping controller design for a GCSC

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IEEE TRANSACTIONS ON POWER DELIVERY 2008年第3期23卷 1673-1681页

作者： Ray, Swakshar Venayagamoorthy, Ganesh Kumar Watanabe, Edson H. Univ Missouri Real Time Power & Intelligent Syst Lab Rolla MO 65409 USA Univ Fed Rio de Janeiro COPPE Dept Elect Engn BR-21941972 Rio De Janeiro Brazil

This paper presents a computational approach for an offline measurement-based design of an optimal damping controller using adaptive critics for a new type of flexible ac transmission system device-the gate-controlled series capacitor (GCSC). Remote measurements are provided to the controller to damp out system modes. The optimal controller is developed based on the heuristic dynamic programming (HDP) approach. Three multilayer-perceptron neural networks are used in the design-the identifier/model network to identify the dynamics of the power system, the critic network to evaluate the performance of the damping controller, and the controller network to provide optimal damping. This measurement-based technique provides an alternative to the classical linear model-based optimal control design. The eigenvalue analysis of the closed-loop system is performed with time-domain responses using the Prony method. An analysis of the simulation results shows potential of the HDP-based optimal damping controller on a GCSC for enhancing the stability of the power system.

关键词： flexible ac transmission systems (FACTS) gate-controlled series capacitor (GCSC) heuristic dynamic programming neurocontroller Prony method remote measurement wide-area control

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Approximate dynamic programming with Gaussian Processes for Optimal Control of Continuous-Time Nonlinear Systems 21st

Approximate Dynamic Programming with Gaussian Processes for ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Beppu, Hirofumi Maruta, Ichiro Fujimoto, Kenji Kyoto Univ Grad Sch Engn Dept Aeronaut & Astronaut Kyoto 6158540 Japan

In this paper, a new algorithm for realization of approximate dynamic programming (ADP) with Gaussian processes (GPs) for continuous-time (CT) nonlinear input-affine systems is proposed to infinite horizon optimal control problems. The convergence for the ADP algorithm is proven based on the assumption of an exact approximation, where both the cost function and the control input converge to their optimal values, that is, the solution to the Hamilton-Jacobi-Bellman (HJB) equation. The approximation errors, however, are unavoidable in almost every case of applications. In order to tackle the problem, the proposed algorithm is derived with the proof of convergence, where the cost function and the control input, which are both approximated, converge to those of the ADP as the number of data points for GPs approaches infinity. A numerical simulation demonstrates the effectiveness of the proposed algorithm. Copyright (C) 2020 The Authors.

关键词： Approximate dynamic programming heuristic dynamic programming value iteration optimal control Gaussian processes nonparametric models

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Adaptive dynamic programming-based Optimal Control of Unknown Affine Nonlinear Discrete-time Systems

Adaptive Dynamic Programming-based Optimal Control of Unknow...

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International Joint Conference on Neural Networks

作者： Dierks, Travis Thumati, Balaje T. Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781424435494

Discrete time approximate dynamic programming (ADP) techniques have been widely used in the recent literature to determine the optimal or near optimal control policies for nonlinear systems. However, an inherent assumption of ADP requires at least partial knowledge of the system dynamics as well as the value of the controlled plant one step ahead. In this work, a novel approach to ADP is attempted while relaxing the need of the partial knowledge of the nonlinear system. The proposed methodology entails a two part process: online system identification and offline optimal control training. First, in the identification process, a neural network (NN) is tuned online to learn the complete plant dynamics and local asymptotic stability is shown under a mild assumption that the NN functional reconstruction errors lie within a small-gain type norm bounded conic sector. Then, using only the NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. Proof of convergence is demonstrated. Simulation results verify theoretical conjecture.

关键词： Nonlinear optimal control heuristic dynamic programming system identification neural network

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Research on the Model and Method for Energy-Saving Generation Dispatching

Research on the Model and Method for Energy-Saving Generatio...

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International Conference on Sustainable Power Generation and Supply

作者： Xie, Guohui Zhang, Lizi He, Xiaoyu N China Elect Power Univ Dept Elect Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781424449347

Energy-Saving generation dispatching can effectively reduce the energy consumption and harmful gas emission, which is of significant social benefits, helps to further clean energy resources development and promote the sustainable development of power system and the whole society. Therefore, it is of theoretical and practical significance. The establishment of a suit of effective models and algorithms for energy-saving generation dispatching is the core to ensure its effective implementation. This paper first expatiates that the implementation of energy-saving generation dispatching is of great significance for the promotion of clean energy resources and the achievement of energy saving and environmental protection goals. Then, after an in-depth study on the theory and practice of the energy-saving generation dispatching, the article analyzes the short-term and long-term feasible development models. That is, the article establishes a short-term mathematical model according to the energy consumption to dispatch, which is designed directly to reduce the system energy consumption as a major objective. Furthermore, from the foreseeable power industry institution in China and the development of energy and environment protection policies, economic optimization mode may be needed to be used in the energy-saving generation dispatching. Therefore, this article establishes a mathematical dispatching model according to energy price in the premise of the implement of related two-part electricity price. The energy price in this model takes various objectives and constraints into consideration such as energy-saving, environmental protection and economy. Afterwards, for the above-mentioned two types of models, this paper proposes a method combined with heuristic dynamic programming and linear programming for the generation output solution of day-ahead generation scheduling. Finally, numerical simulation results in a practical power system demonstrate the correctness and validity of

关键词： Energy-saving generation dispatching Clean energy resources heuristic dynamic programming

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Optimal Control of a Wind Generator System Using Non-Squares Estimators 24

Optimal Control of a Wind Generator System Using Non-Squares...

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24th IEEE International Symposium on Industrial Electronics (ISIE)

作者： Queiroz, Jonathan Araujo Barros, Allan Kardec Neto, Joao Viana da F. Santana, Ewaldo Univ Fed Maranhao Biol Informat Proc Lab Sao Luis Brazil

ISBN: (纸本)9781467375542

The control of eolic and solar energy systems demands methods and technics adapted to the high degree of environment non-stationarities whose adjustments are carried out via adaptive filters. Among the best known are least mean square (LMS) and the recursive least square (RLS) algorithms [1] and [2]. However, those algorithms still fail to respond quickly to the optimal control of the doubly fed induction generator (DFIG) as required in online learning [3]. Here we propose a methodology based on approximate solutions to the linear quadratic regulator (LQR) by using a family of non-squares approximations [4], [5]. We show experimentally that the RLNS provides more accurate estimates for DLQR when compared to the RLS while showing a convergence speed to the actual solution in less than 50% of the iterations as required by the standard RLS estimator for approximating Ricatti equation solution via heuristic dynamic programming (HDP) [6].

关键词： heuristic dynamic programming Discrete Linear Quadratic Regulator Doubly Fed Induction Generator

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Adaptive critic learning techniques for automotive engine control

Adaptive critic learning techniques for automotive engine co...

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American Control Conference

作者： Javaherian, H Liu, D Zhang, Y Kovalenko, O Gen Motors R&D Planning Elect & Controls Integrat Lab Warren MI 48090 USA

ISBN: (纸本)0780383354

A new approach for automotive engine torque and air-fuel ratio control is presented in this paper. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in the present project. Adaptive critic designs are defined as designs that approximate dynamic programming in the general case, i.e., approximate optimal control over time in noisy, nonlinear environment. The present work uses a system, called "critic," to approximate the cost function in dynamic programming and thus to achieve optimal control. The goals of the present learning control design for automotive engines are emissions reduction and maintenance of optimal performance under various operating conditions. Using the data obtained from a test vehicle, we first develop a neural network model of the engine. A neural network controller is then designed based on the idea of approximate dynamic programming to achieve optimal control. In the simulation studies, the initial controller is trained using the neural network engine model developed rather than the actual engine. We have developed and tested self-learning neural network controllers for both engine torque and exhaust air-fuel ratio control. For both control problems, good transient performance of the neural network controller has been observed. A distinct feature of the present technique is the controller's real-time adaptation capability based on real vehicle data which allows the neural network controller to be further refined and improved in real-time vehicle operation through continuous learning.

关键词： neurocontrollers control system synthesis optimal control adaptive control learning systems pollution control dynamic programming internal combustion engines torque control automotive engine control adaptive critic learning techniques air-fuel ratio control heuristic dynamic programming learning control design emissions reduction neural network engine torque

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Discrete Model-Based Adaptive Critic Designs in Wheeled Mobile Robot Control

Discrete Model-Based Adaptive Critic Designs in Wheeled Mobi...

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10th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2010)

作者： Hendzel, Zenon Szuster, Marcin Rzeszow Univ Technol Dept Appl Mech & Robot PL-35959 Rzeszow Poland

ISBN: (纸本)9783642132315

In this paper a discrete tracking control algorithm for a non-holonomic two wheeled mobile robot (WMR) is presented. The basis of the control algorithm is an Adaptive Critic Design (ACD) in two model-based configurations: heuristic dynamic programming (HDP) and Dual heuristic programming (DHP). In proposed control algorithm Actor Critic structure, composed of two neural networks (NN), is supplied by a PD controller and a supervisory term derived from the Lyapunov stability theorem. The control algorithm works on-line and does not require preliminary learning. Verification of the proposed control algorithm was realized on a WMR. Pioneer-2DX.

关键词： Approximate dynamic programming Dual heuristic programming heuristic dynamic programming Neural Networks Tracking Control Wheeled Mobile Robots

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Estimators based on non-squares loss functions to approximate HJB-Riccati equation solution for DLQR design via HDP

Estimators based on non-squares loss functions to approximat...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Queiroz, Jonathan A. Rego, Patricia H. M. Neto, Joao V. F. da Silva, Cristiane Santana, Ewaldo Barros, Allan Kardec Univ Fed Maranhao Embedded Syst & Intelligent Control Lab Sao Luis MA Brazil

ISBN: (纸本)9781479906529

This paper is concerned with the development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation. In the discrete linear quadratic regulator (DLQR) control system design, the HJB equation is the discrete algebraic Riccati (DARE) equation. Due to the problem of dimensionality curse, this equation is approximated via heuristic dynamic programming (HDP). The proposed methodology is based on a familiy of non-squares approximators for critic adaptive solution of the DARE associated to the DLQR problem, referred to in this work as HJB-Riccati equation, which is characterized as a parameterization of the HJB equation. The proposed method is evaluated in a multivariable dynamic system of 4th order with two inputs and it is compared with standard recursive least square algorithm.

关键词： Recursive Least-Squares heuristic dynamic programming Non-squares Approximators Hamilton-Jacobi-Bellman Equation Discrete Algebraic Riccati Equation Discrete Linear Quadratic Regulator

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